Ever since this field was coined and defined in the 1950's, Artificial Intelligence never stopped evolving through academic research to serve the greater cause of providing humankind with the ability to automate most of their daily tasks through a human-like cognitively capable computer. This evolution has touched upon a plethora of fields and most notably the automotive one. This recent popularity of the AI field in automotive market is directly linked with the humongous amounts of data that we have available today.
The autonomous car driving market is expected to reach the value of $11k million by 2025, which surged the adoption and installation rate of AI-based systems in new cars by 109% in 2025. Autonomous AI-powered systems are being standard mainly in two categories:
1. Infotainment human-machine interface, which includes driver's speech, gesture recognition, eye tracking and driver monitoring.
2. Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, including sensors and camera-based machine vision systems.
Behind this revolutionary digital offering sits the newly advanced Deep Learning field with its artificial neural networks, which is derived mainly from Machine Learning to solve most of the unsupervised computer vision, speech recognition and motion detection mimicking in essence the biological human neural networks.
But how does AI function in autonomous vehicles?
At its core, AI powers autonomous cars through an action cycle called AI Perception Action Cycle, which in return encompasses 3 components:
Component 1: In-Vehicle Data Collection & Communication Systems:
This local in-vehicle component is responsible for digital sensorium data gathering generated by the numerous sensors, cameras and motion detection components installed in the vehicle, processes and securely communicates these valuable data to the second component.
Component 2: Autonomous Driving Platform (Cloud)
This component is mainly hosted in the cloud, in order to take advantage of its super powers at the fraction of price, contains an intelligent agent which makes use of advanced AI and deep learning algorithms to make sound decisions. This component is the brain of the autonomous vehicle, and stores all of its driving experiences in a cloud-hosted database.
Component 3: AI-Based Functions in Autonomous Vehicles
Informed by the decisions taken by the intelligent agent, this component is responsible for executing the real-time communications and decision inputs to allow the vehicle to maneuver through traffic without human intervention.
Ultimately, these 3 components form a data loop through which the autonomous car perceive and learn in every subsequent repetition how to better handle complex driving situations.
The recent level that we have reached and tested well in autonomous driving is level 4, which represents high automation and level 5 which represents complete automation is not that far away from its materialization. Now, how our society, government, and urban planners will adopt and act on this ever evolving field of autonomous driving is a question for another time.
A repetitive loop, called Perception Action Cycle, is created when the autonomous vehicle generates data from its surrounding environment and feeds it into the intelligent agent, who in turn makes decisions and enables the autonomous vehicle to perform specific actions in that same environment